# Agile Interception of a Flying Target using Competitive Reinforcement Learning

arXiv: https://arxiv.org/abs/2603.16279 · HTML: https://mlsanity.com/paper/2603.16279/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 14 of its 23 references that are in the map.
- First posted on arXiv 17 Mar 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 14 of 14)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017)
- [Emergent Complexity via Multi-Agent Competition](https://mlsanity.com/paper/1710.03748/) (2017)
- [Learning Quadrotor Control From Visual Features Using Differentiable Simulation](https://mlsanity.com/paper/2410.15979/) (2024)
- [Learning Multi-Pursuit Evasion for Safe Targeted Navigation of Drones](https://mlsanity.com/paper/2304.03443/) (2023)
- [RotorPy: A Python-based Multirotor Simulator with Aerodynamics for Education and Research](https://mlsanity.com/paper/2306.04485/) (2023)
- [Hierarchical Reinforcement Learning for Air Combat at DARPA's AlphaDogfight Trials](https://mlsanity.com/paper/2105.00990/) (2021)
- [Dashing for the Golden Snitch: Multi-Drone Time-Optimal Motion Planning with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2409.16720/) (2024)
- [DACOOP-A: Decentralized Adaptive Cooperative Pursuit via Attention](https://mlsanity.com/paper/2310.15699/) (2023)
- [Towards Safe Mid-Air Drone Interception: Strategies for Tracking & Capture](https://mlsanity.com/paper/2405.13542/) (2024)
- [Online Planning for Multi-UAV Pursuit-Evasion in Unknown Environments Using Deep Reinforcement Learning](https://mlsanity.com/paper/2409.15866/) (2024)
- [Trajectory Generation for Quadrotor Based Systems using Numerical Optimal Control](https://mlsanity.com/paper/1602.01949/) (2016)

## Built on by (most cited first, 1 of 1)

- [Learning Agile Intruder Interception using Differentiable Quadrotor Dynamics](https://mlsanity.com/paper/2607.02472/) (2026)

## Abstract (arXiv)

This article presents a solution to intercept an agile drone by another agile drone carrying a catching net. We formulate the interception as a Competitive Reinforcement Learning problem, where the interceptor and the target drone are controlled by separate policies trained with Proximal Policy Optimization (PPO). We introduce a high-fidelity simulation environment that integrates a realistic quadrotor dynamics model and a low-level control architecture implemented in JAX, which allows for fast parallelized execution on GPUs. We train the agents using low-level control, collective thrust and body rates, to achieve agile flights both for the interceptor and the target. We compare the performance of the trained policies in terms of catch rate, time to catch, and crash rate, against common heuristic baselines and show that our solution outperforms these baselines for interception of agile targets. Finally, we demonstrate the performance of the trained policies in a scaled real-world scenario using agile drones inside an indoor flight arena.